######## snakemake preamble start (automatically inserted, do not edit) ########
import sys;sys.path.extend(['/fh/fast/bloom_j/software/miniforge3/envs/seqneut-pipeline/lib/python3.13/site-packages', '/fh/fast/bloom_j/computational_notebooks/jbloom/2025/flu-seqneut-2025/seqneut-pipeline', '/fh/fast/bloom_j/computational_notebooks/jbloom/2025/flu-seqneut-2025', '/fh/fast/bloom_j/software/miniforge3/envs/seqneut-pipeline/bin', '/fh/fast/bloom_j/software/miniforge3/envs/seqneut-pipeline/lib/python3.13', '/fh/fast/bloom_j/software/miniforge3/envs/seqneut-pipeline/lib/python3.13/lib-dynload', '/fh/fast/bloom_j/software/miniforge3/envs/seqneut-pipeline/lib/python3.13/site-packages', '/home/jbloom/.cache/snakemake/snakemake/source-cache/runtime-cache/tmpfro_n8p6/file/fh/fast/bloom_j/computational_notebooks/jbloom/2025/flu-seqneut-2025/seqneut-pipeline/notebooks', '/fh/fast/bloom_j/computational_notebooks/jbloom/2025/flu-seqneut-2025/seqneut-pipeline/notebooks']);import pickle;from snakemake import script;script.snakemake = pickle.loads(b'\x80\x04\x95 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script;from snakemake.logging import logger;from snakemake.script import snakemake;import os; os.chdir(r'/fh/fast/bloom_j/computational_notebooks/jbloom/2025/flu-seqneut-2025');
######## snakemake preamble end #########

Process plate counts to get fraction infectivities and fit curves

This notebook is designed to be run using snakemake, and analyzes a plate of sequencing-based neutralization assays.

The plots generated by this notebook are interactive, so you can mouseover points for details, use the mouse-scroll to zoom and pan, and use interactive dropdowns at the bottom of the plots.

Setup

Import Python modules:

import pickle
import sys
import warnings

import altair as alt

import matplotlib.pyplot as plt
import matplotlib.style

import neutcurve
from neutcurve.colorschemes import CBPALETTE, CBMARKERS

import numpy

import pandas as pd

import ruamel.yaml as yaml

_ = alt.data_transformers.disable_max_rows()

# avoid clutter w RuntimeWarning during curve fitting
warnings.filterwarnings("ignore", category=RuntimeWarning)

# faster plotting of neut curves
matplotlib.style.use("fast")

Get the variables passed by snakemake:

count_csvs = snakemake.input.count_csvs
fate_csvs = snakemake.input.fate_csvs
notebook_funcs = snakemake.input.notebook_funcs
qc_drops_yaml = snakemake.output.qc_drops
frac_infectivity_csv = snakemake.output.frac_infectivity_csv
fits_csv = snakemake.output.fits_csv
fits_pickle = snakemake.output.fits_pickle
viral_barcodes = snakemake.params.viral_barcodes
neut_standard_barcodes = snakemake.params.neut_standard_barcodes
samples = snakemake.params.samples
plate = snakemake.wildcards.plate
plate_params = snakemake.params.plate_params
curve_display_method = snakemake.params.curve_display_method

# get thresholds turning lists into tuples as needed
manual_drops = {
    filter_type: [tuple(w) if isinstance(w, list) else w for w in filter_drops]
    for (filter_type, filter_drops) in plate_params["manual_drops"].items()
}
group = plate_params["group"]
qc_thresholds = plate_params["qc_thresholds"]
curvefit_params = plate_params["curvefit_params"]
curvefit_qc = plate_params["curvefit_qc"]
curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"] = [
    tuple(w) for w in curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"]
]

print(f"Processing {plate=}")

samples_df = pd.DataFrame(plate_params["samples"])
print(f"\nPlate has {len(samples)} samples (wells)")
assert all(
    (len(samples_df) == samples_df[c].nunique())
    for c in ["well", "sample", "sample_noplate"]
)
assert len(samples_df) == len(
    samples_df.groupby(["serum_replicate", "dilution_factor"])
)
assert len(samples) == len(count_csvs) == len(fate_csvs) == len(samples_df)

for d, key, title in [
    (manual_drops, "manual_drops", "Data manually specified to drop:"),
    (qc_thresholds, "qc_thresholds", "QC thresholds applied to data:"),
    (curvefit_params, "curvefit_params", "Curve-fitting parameters:"),
    (curvefit_qc, "curvefit_qc", "Curve-fitting QC:"),
]:
    print(f"\n{title}")
    yaml.YAML(typ="rt").dump({key: d}, stream=sys.stdout)

Processing plate='plate7_FCI'

Plate has 64 samples (wells)

Data manually specified to drop:
manual_drops: {}

QC thresholds applied to data:
qc_thresholds:
  avg_barcode_counts_per_well: 500
  min_neut_standard_frac_per_well: 0.005
  no_serum_per_viral_barcode_filters:
    min_frac: 0.0001
    max_fold_change: 4
    max_wells: 2
  per_neut_standard_barcode_filters:
    min_frac: 0.005
    max_fold_change: 4
    max_wells: 2
  min_neut_standard_count_per_well: 1000
  min_no_serum_count_per_viral_barcode_well: 100
  max_frac_infectivity_per_viral_barcode_well: 3
  min_dilutions_per_barcode_serum_replicate: 6

Curve-fitting parameters:
curvefit_params:
  frac_infectivity_ceiling: 1
  fixtop:
  - 0.6
  - 1
  fixbottom: 0
  fixslope:
  - 0.8
  - 10

Curve-fitting QC:
curvefit_qc:
  max_frac_infectivity_at_least: 0.0
  goodness_of_fit:
    min_R2: 0.5
    max_RMSD: 0.15
  serum_replicates_ignore_curvefit_qc: []
  barcode_serum_replicates_ignore_curvefit_qc: []

Load the notebook functions:

print(f"Loading {notebook_funcs=}")
%run {notebook_funcs}

Loading notebook_funcs='/home/jbloom/.cache/snakemake/snakemake/source-cache/runtime-cache/tmpfro_n8p6/file/fh/fast/bloom_j/computational_notebooks/jbloom/2025/flu-seqneut-2025/seqneut-pipeline/notebook_funcs.py'

Set up dictionary to keep track of wells, barcodes, well-barcodes, and serum-replicates that are dropped:

qc_drops = {
    "wells": {},
    "barcodes": {},
    "barcode_wells": {},
    "barcode_serum_replicates": {},
    "serum_replicates": {},
}

assert set(manual_drops).issubset(
    qc_drops
), f"{manual_drops.keys()=}, {qc_drops.keys()}"

Statistics on barcode-parsing for each sample

Make interactive chart of the "fates" of the sequencing reads parsed for each sample on the plate.

If most sequencing reads are not "valid barcodes", this could potentially indicate some problem in the sequencing or barcode set you are parsing.

Potential fates are:

• valid barcode: barcode that matches a known virus or neutralization standard, we hope most reads are this.
• invalid barcode: a barcode with proper flanking sequences, but does not match a known virus or neutralization standard. If you have a lot of reads of this type, it is probably a good idea to look at the invalid barcode CSVs (in the ./results/barcode_invalid/ subdirectory created by the pipeline) to see what these invalid barcodes are.
• unparseable barcode: could not parse a barcode from this read as there was not a sequence of the correct length with the appropriate flanking sequence.
• invalid outer flank: if using an outer upstream or downstream region (upstream2 or downstream2 for the illuminabarcodeparser), reads that are otherwise valid except for this outer flank. Typically you would be using upstream2 if you have a plate index embedded in your primer, and reads with this classification correspond to a different index than the one for this plate.
• low quality barcode: low-quality or N nucleotides in barcode, could indicate problem with sequencing.
• failed chastity filter: reads that failed the Illumina chastity filter, if these are reported in the FASTQ (they may not be).

Also, if the number of reads per sample is very uneven, that could indicate that you did not do a good job of balancing the different samples in the Illumina sequencing.

fates = (
    pd.concat([pd.read_csv(f).assign(sample=s) for f, s in zip(fate_csvs, samples)])
    .merge(samples_df, validate="many_to_one", on="sample")
    .assign(
        fate_counts=lambda x: x.groupby("fate")["count"].transform("sum"),
        sample_well=lambda x: x["sample_noplate"] + " (" + x["well"] + ")",
    )
    .query("fate_counts > 0")[  # only keep fates with at least one count
        ["fate", "count", "well", "serum_replicate", "sample_well", "dilution_factor"]
    ]
)

assert len(fates) == len(fates.drop_duplicates())

serum_replicates = sorted(fates["serum_replicate"].unique())
sample_wells = list(
    fates.sort_values(["serum_replicate", "dilution_factor"])["sample_well"]
)


serum_selection = alt.selection_point(
    fields=["serum_replicate"],
    bind=alt.binding_select(
        options=[None] + serum_replicates,
        labels=["all"] + serum_replicates,
        name="serum",
    ),
)

fates_chart = (
    alt.Chart(fates)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X("count", scale=alt.Scale(nice=False, padding=3)),
        alt.Y(
            "sample_well",
            title=None,
            sort=sample_wells,
        ),
        alt.Color("fate", sort=sorted(fates["fate"].unique(), reverse=True)),
        alt.Order("fate", sort="descending"),
        tooltip=fates.columns.tolist(),
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=200,
        title=f"Barcode parsing for {plate}",
    )
    .configure_axis(grid=False)
)

fates_chart

Read barcode counts and apply manually specified drops

Read the counts per barcode:

# get barcode counts
counts = (
    pd.concat([pd.read_csv(c).assign(sample=s) for c, s in zip(count_csvs, samples)])
    .merge(samples_df, validate="many_to_one", on="sample")
    .drop(columns=["replicate", "plate", "fastq"])
    .assign(sample_well=lambda x: x["sample_noplate"] + " (" + x["well"] + ")")
)

# classify barcodes as viral or neut standard
barcode_class = pd.concat(
    [
        pd.DataFrame(viral_barcodes).assign(neut_standard=False),
        pd.DataFrame(neut_standard_barcodes).assign(neut_standard=True, strain=pd.NA),
    ],
    ignore_index=True,
)

# merge counts and classification of barcodes
assert set(counts["barcode"]) == set(barcode_class["barcode"])
counts = counts.merge(barcode_class, on="barcode", validate="many_to_one")
assert set(sample_wells) == set(counts["sample_well"])
assert set(serum_replicates) == set(counts["serum_replicate"])

Apply any manually specified data drops:

for filter_type, filter_drops in manual_drops.items():
    print(f"\nDropping {len(filter_drops)} {filter_type} specified in manual_drops")
    assert filter_type in qc_drops
    qc_drops[filter_type].update(
        {w: "manual_drop" for w in filter_drops if not isinstance(w, list)}
    )
    if filter_type == "barcode_wells":
        counts = counts[
            ~counts.assign(
                barcode_well=lambda x: x.apply(
                    lambda r: (r["barcode"], r["well"]), axis=1
                )
            )["barcode_well"].isin(qc_drops[filter_type])
        ]
    elif filter_type == "barcode_serum_replicates":
        counts = counts[
            ~counts.assign(
                barcode_serum_replicate=lambda x: x.apply(
                    lambda r: (r["barcode"], r["serum_replicate"]), axis=1
                )
            )["barcode_serum_replicate"].isin(qc_drops[filter_type])
        ]
    elif filter_type == "wells":
        counts = counts[~counts["well"].isin(qc_drops[filter_type])]
    elif filter_type == "barcodes":
        counts = counts[~counts["barcode"].isin(qc_drops[filter_type])]
    elif filter_type == "serum_replicates":
        counts = counts[~counts["serum_replicate"].isin(qc_drops[filter_type])]
    elif filter_type == "barcode_serum_replicates":
        counts = counts[~counts["barcode_serum_replicate"].isin(qc_drops[filter_type])]
    else:
        assert filter_type in set(counts.columns)
        counts = counts[~counts[filter_type].isin(qc_drops[filter_type])]

Average counts per barcode in each well

Plot average counts per barcode. If a sample has inadequate barcode counts, it may not have good enough statistics for accurate analysis, and a QC-threshold is applied:

avg_barcode_counts = (
    counts.groupby(
        ["well", "serum_replicate", "sample_well"],
        dropna=False,
        as_index=False,
    )
    .aggregate(avg_count=pd.NamedAgg("count", "mean"))
    .assign(
        fails_qc=lambda x: (
            x["avg_count"] < qc_thresholds["avg_barcode_counts_per_well"]
        ),
    )
)

avg_barcode_counts_chart = (
    alt.Chart(avg_barcode_counts)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X(
            "avg_count",
            title="average barcode counts per well",
            scale=alt.Scale(nice=False, padding=3),
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Color(
            "fails_qc",
            title=f"fails {qc_thresholds['avg_barcode_counts_per_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        tooltip=[
            alt.Tooltip(c, format=".3g") if avg_barcode_counts[c].dtype == float else c
            for c in avg_barcode_counts.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=250,
        title=f"Average barcode counts per well for {plate}",
    )
    .configure_axis(grid=False)
)

display(avg_barcode_counts_chart)

# drop wells failing QC
avg_barcode_counts_per_well_drops = list(avg_barcode_counts.query("fails_qc")["well"])
print(
    f"\nDropping {len(avg_barcode_counts_per_well_drops)} wells for failing "
    f"{qc_thresholds['avg_barcode_counts_per_well']=}: "
    + str(avg_barcode_counts_per_well_drops)
)
qc_drops["wells"].update(
    {w: "avg_barcode_counts_per_well" for w in avg_barcode_counts_per_well_drops}
)
counts = counts[~counts["well"].isin(qc_drops["wells"])]

Dropping 1 wells for failing qc_thresholds['avg_barcode_counts_per_well']=500: ['F2']

Fraction of counts from neutralization standard

Determine the fraction of counts from the neutralization standard in each sample, and make sure this fraction passess the QC threshold.

neut_standard_fracs = (
    counts.assign(
        neut_standard_count=lambda x: x["count"] * x["neut_standard"].astype(int)
    )
    .groupby(
        ["well", "serum_replicate", "sample_well"],
        dropna=False,
        as_index=False,
    )
    .aggregate(
        total_count=pd.NamedAgg("count", "sum"),
        neut_standard_count=pd.NamedAgg("neut_standard_count", "sum"),
    )
    .assign(
        neut_standard_frac=lambda x: x["neut_standard_count"] / x["total_count"],
        fails_qc=lambda x: (
            x["neut_standard_frac"] < qc_thresholds["min_neut_standard_frac_per_well"]
        ),
    )
)

neut_standard_fracs_chart = (
    alt.Chart(neut_standard_fracs)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X(
            "neut_standard_frac",
            title="frac counts from neutralization standard per well",
            scale=alt.Scale(nice=False, padding=3),
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Color(
            "fails_qc",
            title=f"fails {qc_thresholds['min_neut_standard_frac_per_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        tooltip=[
            alt.Tooltip(c, format=".3g") if neut_standard_fracs[c].dtype == float else c
            for c in neut_standard_fracs.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=250,
        title=f"Neutralization-standard fracs per well for {plate}",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
)

display(neut_standard_fracs_chart)

# drop wells failing QC
min_neut_standard_frac_per_well_drops = list(
    neut_standard_fracs.query("fails_qc")["well"]
)
print(
    f"\nDropping {len(min_neut_standard_frac_per_well_drops)} wells for failing "
    f"{qc_thresholds['min_neut_standard_frac_per_well']=}: "
    + str(min_neut_standard_frac_per_well_drops)
)
qc_drops["wells"].update(
    {
        w: "min_neut_standard_frac_per_well"
        for w in min_neut_standard_frac_per_well_drops
    }
)
counts = counts[~counts["well"].isin(qc_drops["wells"])]

Dropping 0 wells for failing qc_thresholds['min_neut_standard_frac_per_well']=0.005: []

Consistency and minimum fractions for barcodes

We examine the fraction of counts attributable to each barcode. We do this splitting the data two ways:

1. Looking at all viral (but not neut-standard) barcodes only for the no-serum samples (wells).

2. Looking at just the neut-standard barcodes for all samples (wells).

The reasons is that if the experiment is set up perfectly, these fractions should be the same across all samples for each barcode. (We do not expect viral barcodes to have consistent fractions across no-serum samples as they will be neutralized differently depending on strain).

We plot these fractions in interactive plots (you can mouseover points and zoom) so you can identify barcodes that fail the expected consistency QC thresholds.

We also make sure the barcodes meet specified QC minimum thresholds for all samples, and flag any that do not.

barcode_selection = alt.selection_point(fields=["barcode"], on="mouseover", empty=False)

# look at all samples for neut standard barcodes, or no-serum samples for all barcodes
for is_neut_standard, df in counts.groupby("neut_standard"):
    if is_neut_standard:
        print(
            f"\n\n{'=' * 89}\nAnalyzing neut-standard barcodes from all samples (wells)"
        )
        qc_name = "per_neut_standard_barcode_filters"
    else:
        print(f"\n\n{'=' * 89}\nAnalyzing all barcodes from no-serum samples (wells)")
        qc_name = "no_serum_per_viral_barcode_filters"
        df = df.query("serum == 'none'")

    df = df.assign(
        sample_counts=lambda x: x.groupby("sample")["count"].transform("sum"),
        count_frac=lambda x: x["count"] / x["sample_counts"],
        median_count_frac=lambda x: x.groupby("barcode")["count_frac"].transform(
            "median"
        ),
        fold_change_from_median=lambda x: numpy.where(
            x["count_frac"] > x["median_count_frac"],
            x["count_frac"] / x["median_count_frac"],
            x["median_count_frac"] / x["count_frac"],
        ),
    )[
        [
            "barcode",
            "count",
            "sample_well",
            "count_frac",
            "fold_change_from_median",
        ]
        + ([] if is_neut_standard else ["strain"])
    ]

    # barcode fails QC if fails in sufficient wells
    qc = qc_thresholds[qc_name]
    print(f"Apply QC {qc_name}: {qc}\n")
    fails_qc = (
        df.assign(
            fails_qc=lambda x: ~(
                (x["count_frac"] >= qc["min_frac"])
                & (x["fold_change_from_median"] <= qc["max_fold_change"])
            ),
        )
        .groupby("barcode", as_index=False)
        .aggregate(n_wells_fail_qc=pd.NamedAgg("fails_qc", "sum"))
        .assign(fails_qc=lambda x: x["n_wells_fail_qc"] >= qc["max_wells"])[
            ["barcode", "fails_qc"]
        ]
    )
    df = df.merge(fails_qc, on="barcode", validate="many_to_one")

    # make chart
    evenness_chart = (
        alt.Chart(df)
        .add_params(barcode_selection)
        .encode(
            alt.X(
                "count_frac",
                title=(
                    "barcode's fraction of neut standard counts"
                    if is_neut_standard
                    else "barcode's fraction of non-neut standard counts"
                ),
                scale=alt.Scale(nice=False, padding=5),
            ),
            alt.Y("sample_well", sort=sample_wells),
            alt.Fill(
                "fails_qc",
                title=f"fails {qc_name}",
                legend=alt.Legend(titleLimit=500),
            ),
            strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
            size=alt.condition(barcode_selection, alt.value(60), alt.value(35)),
            tooltip=[
                alt.Tooltip(c, format=".2g") if df[c].dtype == float else c
                for c in df.columns
            ],
        )
        .mark_circle(fillOpacity=0.45, stroke="black", strokeOpacity=1)
        .properties(
            height=alt.Step(10),
            width=300,
            title=alt.TitleParams(
                (
                    f"{plate} all samples, neut-standard barcodes"
                    if is_neut_standard
                    else f"{plate} no-serum samples, all barcodes"
                ),
                subtitle="x-axis is zoomable (use mouse scroll/pan)",
            ),
        )
        .configure_axis(grid=False)
        .configure_legend(titleLimit=1000)
        .interactive()
    )

    display(evenness_chart)

    # drop barcodes failing QC
    barcode_drops = list(fails_qc.query("fails_qc")["barcode"])
    print(
        f"\nDropping {len(barcode_drops)} barcodes for failing {qc=}: {barcode_drops}"
    )
    qc_drops["barcodes"].update(
        {bc: "min_neut_standard_frac_per_well" for bc in barcode_drops}
    )
    counts = counts[~counts["barcode"].isin(qc_drops["barcodes"])]

=========================================================================================
Analyzing all barcodes from no-serum samples (wells)
Apply QC no_serum_per_viral_barcode_filters: {'min_frac': 0.0001, 'max_fold_change': 4, 'max_wells': 2}

Dropping 0 barcodes for failing qc={'min_frac': 0.0001, 'max_fold_change': 4, 'max_wells': 2}: []


=========================================================================================
Analyzing neut-standard barcodes from all samples (wells)
Apply QC per_neut_standard_barcode_filters: {'min_frac': 0.005, 'max_fold_change': 4, 'max_wells': 2}

Dropping 0 barcodes for failing qc={'min_frac': 0.005, 'max_fold_change': 4, 'max_wells': 2}: []

Compute fraction infectivity

The fraction infectivity for viral barcode $v_b$ in sample $s$ is computed as: $$ F_{v_b,s} = \frac{c_{v_b,s} / \left(\sum_{n_b} c_{n_b,s}\right)}{{\rm median}_{s_0}\left[ c_{v_b,s_0} / \left(\sum_{n_b} c_{n_b,s_0}\right)\right]} $$ where

• $c_{v_b,s}$ is the counts of viral barcode $v_b$ in sample $s$.
• $\sum_{n_b} c_{n_b,s}$ is the sum of the counts for all neutralization standard barcodes $n_b$ for sample $s$.
• $c_{v_b,s_0}$ is the counts of viral barcode $v_b$ in no-serum sample $s_0$.
• $\sum_{n_b} c_{n_b,s_0}$ is the sum of the counts for all neutralization standard barcodes $n_b$ for no-serum sample $s_0$.
• ${\rm median}_{s_0}\left[ c_{v_b,s_0} / \left(\sum_{n_b} c_{n_b,s_0}\right)\right]$ is the median taken across all no-serum samples of the counts of viral barcode $v_b$ versus the total counts for all neutralization standard barcodes.

First, compute the total neutralization-standard counts for each sample (well). Plot these, and drop any wells that do not meet the QC threshold.

neut_standard_counts = (
    counts.query("neut_standard")
    .groupby(
        ["well", "serum_replicate", "sample_well", "dilution_factor"],
        dropna=False,
        as_index=False,
    )
    .aggregate(neut_standard_count=pd.NamedAgg("count", "sum"))
    .assign(
        fails_qc=lambda x: (
            x["neut_standard_count"] < qc_thresholds["min_neut_standard_count_per_well"]
        ),
    )
)

neut_standard_counts_chart = (
    alt.Chart(neut_standard_counts)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X(
            "neut_standard_count",
            title="counts from neutralization standard",
            scale=alt.Scale(nice=False, padding=3),
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Color(
            "fails_qc",
            title=f"fails {qc_thresholds['min_neut_standard_count_per_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        tooltip=[
            (
                alt.Tooltip(c, format=".3g")
                if neut_standard_counts[c].dtype == float
                else c
            )
            for c in neut_standard_counts.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=250,
        title=f"Neutralization-standard counts for {plate}",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
)

display(neut_standard_counts_chart)

# drop wells failing QC
min_neut_standard_count_per_well_drops = list(
    neut_standard_counts.query("fails_qc")["well"]
)
print(
    f"\nDropping {len(min_neut_standard_count_per_well_drops)} wells for failing "
    f"{qc_thresholds['min_neut_standard_count_per_well']=}: "
    + str(min_neut_standard_count_per_well_drops)
)
qc_drops["wells"].update(
    {
        w: "min_neut_standard_count_per_well"
        for w in min_neut_standard_count_per_well_drops
    }
)
neut_standard_counts = neut_standard_counts[
    ~neut_standard_counts["well"].isin(qc_drops["wells"])
]
counts = counts[~counts["well"].isin(qc_drops["wells"])]

Dropping 0 wells for failing qc_thresholds['min_neut_standard_count_per_well']=1000: []

Compute and plot the no-serum sample viral barcode counts and check if they pass the QC filters.

no_serum_counts = (
    counts.query("serum == 'none'")
    .query("not neut_standard")
    .merge(neut_standard_counts, validate="many_to_one")[
        ["barcode", "strain", "well", "sample_well", "count", "neut_standard_count"]
    ]
    .assign(
        fails_qc=lambda x: (
            x["count"] <= qc_thresholds["min_no_serum_count_per_viral_barcode_well"]
        ),
    )
)

strains = sorted(no_serum_counts["strain"].unique())
strain_selection_dropdown = alt.selection_point(
    fields=["strain"],
    bind=alt.binding_select(
        options=[None] + strains,
        labels=["all"] + strains,
        name="virus strain",
    ),
)

# make chart
no_serum_counts_plot_df = no_serum_counts.drop(columns=["well", "neut_standard_count"])
no_serum_counts_chart = (
    alt.Chart(no_serum_counts_plot_df)
    .add_params(barcode_selection, strain_selection_dropdown)
    .transform_filter(strain_selection_dropdown)
    .encode(
        alt.X(
            "count", title="viral barcode count", scale=alt.Scale(nice=False, padding=5)
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Fill(
            "fails_qc",
            title=f"fails {qc_thresholds['min_no_serum_count_per_viral_barcode_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
        size=alt.condition(barcode_selection, alt.value(60), alt.value(35)),
        tooltip=no_serum_counts_plot_df.columns.tolist(),
    )
    .mark_circle(fillOpacity=0.6, stroke="black", strokeOpacity=1)
    .properties(
        height=alt.Step(10),
        width=400,
        title=f"{plate} viral barcode counts in no-serum samples",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
    .interactive()
)

display(no_serum_counts_chart)

# drop barcode / wells failing QC
min_no_serum_count_per_viral_barcode_well_drops = list(
    no_serum_counts.query("fails_qc")[["barcode", "well"]].itertuples(
        index=False, name=None
    )
)
print(
    f"\nDropping {len(min_no_serum_count_per_viral_barcode_well_drops)} barcode-wells for failing "
    f"{qc_thresholds['min_no_serum_count_per_viral_barcode_well']=}: "
    + str(min_no_serum_count_per_viral_barcode_well_drops)
)
qc_drops["barcode_wells"].update(
    {
        w: "min_no_serum_count_per_viral_barcode_well"
        for w in min_no_serum_count_per_viral_barcode_well_drops
    }
)
no_serum_counts = no_serum_counts[
    ~no_serum_counts.assign(
        barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
    )["barcode_well"].isin(qc_drops["barcode_wells"])
]
counts = counts[
    ~counts.assign(
        barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
    )["barcode_well"].isin(qc_drops["barcode_wells"])
]

Dropping 0 barcode-wells for failing qc_thresholds['min_no_serum_count_per_viral_barcode_well']=100: []

Compute and plot the median ratio of viral barcode count to neut standard counts across no-serum samples. If library composition is equal, all of these values should be similar:

median_no_serum_ratio = (
    no_serum_counts.assign(ratio=lambda x: x["count"] / x["neut_standard_count"])
    .groupby(["barcode", "strain"], as_index=False)
    .aggregate(median_no_serum_ratio=pd.NamedAgg("ratio", "median"))
)

strain_selection = alt.selection_point(fields=["strain"], on="mouseover", empty=False)

median_no_serum_ratio_chart = (
    alt.Chart(median_no_serum_ratio)
    .add_params(strain_selection)
    .encode(
        alt.X(
            "median_no_serum_ratio",
            title="median ratio of counts",
            scale=alt.Scale(nice=False, padding=5),
        ),
        alt.Y(
            "barcode",
            sort=alt.SortField("median_no_serum_ratio", order="descending"),
            axis=alt.Axis(labelFontSize=5),
        ),
        color=alt.condition(strain_selection, alt.value("orange"), alt.value("gray")),
        tooltip=[
            (
                alt.Tooltip(c, format=".3g")
                if median_no_serum_ratio[c].dtype == float
                else c
            )
            for c in median_no_serum_ratio.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(5),
        width=250,
        title=f"{plate} no-serum median ratio viral barcode to neut-standard barcode",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
)

display(median_no_serum_ratio_chart)

Compute the actual fraction infectivities. We compute both the raw fraction infectivities and the ones with the ceiling applied:

frac_infectivity = (
    counts.query("not neut_standard")
    .query("serum != 'none'")
    .merge(median_no_serum_ratio, validate="many_to_one")
    .merge(neut_standard_counts, validate="many_to_one")
    .assign(
        frac_infectivity_raw=lambda x: (
            (x["count"] / x["neut_standard_count"]) / x["median_no_serum_ratio"]
        ),
        frac_infectivity_ceiling=lambda x: x["frac_infectivity_raw"].clip(
            upper=curvefit_params["frac_infectivity_ceiling"]
        ),
        concentration=lambda x: 1 / x["dilution_factor"],
        plate_barcode=lambda x: x["plate_replicate"] + "-" + x["barcode"],
    )[
        [
            "barcode",
            "plate_barcode",
            "well",
            "strain",
            "serum",
            "serum_replicate",
            "dilution_factor",
            "concentration",
            "frac_infectivity_raw",
            "frac_infectivity_ceiling",
        ]
    ]
)

assert len(
    frac_infectivity.groupby(["serum", "plate_barcode", "dilution_factor"])
) == len(frac_infectivity)
assert frac_infectivity["dilution_factor"].notnull().all()
assert frac_infectivity["frac_infectivity_raw"].notnull().all()
assert frac_infectivity["frac_infectivity_ceiling"].notnull().all()

Plot the fraction infectivities, both the raw values and with the ceiling applied:

frac_infectivity_cols = {
    "frac_infectivity_raw": "raw fraction infectivity",
    "frac_infectivity_ceiling": f"fraction infectivity with ceiling at {curvefit_params['frac_infectivity_ceiling']}",
}

frac_infectivity_chart_df = frac_infectivity.assign(
    fails_qc=lambda x: (
        x["frac_infectivity_raw"]
        > qc_thresholds["max_frac_infectivity_per_viral_barcode_well"]
    ),
)[
    [
        "barcode",
        "strain",
        "well",
        "serum_replicate",
        "dilution_factor",
        "fails_qc",
        *list(frac_infectivity_cols),
    ]
].rename(
    columns=frac_infectivity_cols
)

# some manipulations to shrink data frame plotted by altair below by putting
# them in smaller data frames that are used via transform_lookup
barcode_lookup_df = frac_infectivity[["barcode", "strain"]].drop_duplicates()
assert len(barcode_lookup_df) == barcode_lookup_df["barcode"].nunique()
well_lookup_df = frac_infectivity[
    ["well", "serum_replicate", "dilution_factor"]
].drop_duplicates()
assert len(well_lookup_df) == well_lookup_df["well"].nunique()

frac_infectivity_chart_df = frac_infectivity_chart_df.drop(
    columns=["strain", "serum_replicate", "dilution_factor"]
)

frac_infectivity_chart = (
    alt.Chart(frac_infectivity_chart_df)
    .transform_lookup(
        lookup="barcode",
        from_=alt.LookupData(barcode_lookup_df, key="barcode", fields=["strain"]),
    )
    .transform_lookup(
        lookup="well",
        from_=alt.LookupData(
            well_lookup_df, key="well", fields=["serum_replicate", "dilution_factor"]
        ),
    )
    .transform_fold(
        frac_infectivity_cols.values(), ["ceiling_applied", "frac_infectivity"]
    )
    .add_params(strain_selection_dropdown, barcode_selection)
    .transform_filter(strain_selection_dropdown)
    .encode(
        alt.X(
            "dilution_factor:Q",
            title="dilution factor",
            scale=alt.Scale(nice=False, padding=5, type="log"),
        ),
        alt.Y(
            "frac_infectivity:Q",
            title="fraction infectivity",
            scale=alt.Scale(nice=False, padding=5),
        ),
        alt.Column(
            "ceiling_applied:N",
            sort="descending",
            title=None,
            header=alt.Header(labelFontSize=13, labelFontStyle="bold", labelPadding=2),
        ),
        alt.Row(
            "serum_replicate:N",
            title=None,
            spacing=3,
            header=alt.Header(labelFontSize=13, labelFontStyle="bold"),
        ),
        alt.Detail("barcode"),
        alt.Shape(
            "fails_qc",
            title=f"fails {qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=}",
            legend=alt.Legend(titleLimit=500, orient="bottom"),
        ),
        color=alt.condition(
            barcode_selection, alt.value("black"), alt.value("MediumBlue")
        ),
        strokeWidth=alt.condition(barcode_selection, alt.value(3), alt.value(1)),
        opacity=alt.condition(barcode_selection, alt.value(1), alt.value(0.25)),
        tooltip=[
            (
                alt.Tooltip(c, format=".3g")
                if frac_infectivity_chart_df[c].dtype == float
                else c
            )
            for c in frac_infectivity_chart_df.columns
        ]
        + [
            alt.Tooltip("strain:N"),
            alt.Tooltip("serum_replicate:N"),
            alt.Tooltip("dilution_factor:Q"),
        ],
    )
    .mark_line(point=True)
    .properties(
        height=150,
        width=250,
        title=f"Fraction infectivities for {plate}",
    )
    .interactive(bind_x=False)
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
    .configure_point(size=50)
    .resolve_scale(x="independent", y="independent")
)

display(frac_infectivity_chart)

# drop barcode / wells failing QC
max_frac_infectivity_per_viral_barcode_well_drops = list(
    frac_infectivity_chart_df.query("fails_qc")[["barcode", "well"]]
    .drop_duplicates()
    .itertuples(index=False, name=None)
)
print(
    f"\nDropping {len(max_frac_infectivity_per_viral_barcode_well_drops)} barcode-wells for failing "
    f"{qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=}: "
    + str(max_frac_infectivity_per_viral_barcode_well_drops)
)
qc_drops["barcode_wells"].update(
    {
        w: "max_frac_infectivity_per_viral_barcode_well"
        for w in max_frac_infectivity_per_viral_barcode_well_drops
    }
)
frac_infectivity = frac_infectivity[
    ~frac_infectivity.assign(
        barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
    )["barcode_well"].isin(qc_drops["barcode_wells"])
]

Dropping 26 barcode-wells for failing qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=3: [('CCTTTCTCAAAACATA', 'A1'), ('CCTTTCTCAAAACATA', 'E1'), ('CCTTTCTCAAAACATA', 'H1'), ('CCTTTCTCAAAACATA', 'B2'), ('TGTTGTAATCTGAATA', 'A3'), ('CCTTTCTCAAAACATA', 'A3'), ('ATTTACTCATTATACG', 'B3'), ('GATTCAGATGCCCACC', 'D3'), ('TATCCAAGGGACGGAC', 'E3'), ('CGATCTTTACGAAAAA', 'E3'), ('GCCGCTGCGGCGTGTG', 'F3'), ('AGTCCTATCCTCAAAT', 'G3'), ('CCTTTCTCAAAACATA', 'A4'), ('CCTTTCTCAAAACATA', 'D4'), ('TCACGACTCGACTAAC', 'F4'), ('TGTTGTAATCTGAATA', 'B5'), ('GATCGCCACTGATAAG', 'C5'), ('CCTTTCTCAAAACATA', 'C5'), ('GTAATTCGCATGCGGA', 'D5'), ('GATTCAGATGCCCACC', 'E5'), ('CCTTTCTCAAAACATA', 'F5'), ('CCGCATTAGCGGGAGG', 'D6'), ('CACAGACAATAAAAAA', 'G6'), ('CTCTTACGCTCCTACG', 'G6'), ('TTGACTCACCGAATAA', 'H6'), ('AAAGACCTTTAACTCT', 'H7')]

Check how many dilutions we have per barcode / serum-replicate:

n_dilutions = (
    frac_infectivity.groupby(["serum_replicate", "strain", "barcode"], as_index=False)
    .aggregate(**{"number of dilutions": pd.NamedAgg("dilution_factor", "nunique")})
    .assign(
        fails_qc=lambda x: (
            x["number of dilutions"]
            < qc_thresholds["min_dilutions_per_barcode_serum_replicate"]
        ),
    )
)

n_dilutions_chart = (
    alt.Chart(n_dilutions)
    .add_params(barcode_selection)
    .encode(
        alt.X("number of dilutions", scale=alt.Scale(nice=False, padding=4)),
        alt.Y("strain", title=None),
        alt.Column(
            "serum_replicate",
            title=None,
            header=alt.Header(labelFontSize=12, labelFontStyle="bold", labelPadding=0),
        ),
        alt.Fill(
            "fails_qc",
            title=f"fails {qc_thresholds['min_dilutions_per_barcode_serum_replicate']=}",
            legend=alt.Legend(titleLimit=500, orient="bottom"),
        ),
        strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
        size=alt.condition(barcode_selection, alt.value(55), alt.value(35)),
        tooltip=[
            alt.Tooltip(c, format=".3g") if n_dilutions[c].dtype == float else c
            for c in n_dilutions.columns
        ],
    )
    .mark_circle(stroke="black", strokeOpacity=1, fillOpacity=0.45)
    .properties(
        height=alt.Step(10),
        width=120,
        title=alt.TitleParams(
            "number of dilutions for each barcode for each serum-replicate", dy=-2
        ),
    )
)

display(n_dilutions_chart)

# drop barcode / serum-replicates failing QC
min_dilutions_per_barcode_serum_replicate_drops = list(
    n_dilutions.query("fails_qc")[["barcode", "serum_replicate"]].itertuples(
        index=False, name=None
    )
)
print(
    f"\nDropping {len(min_dilutions_per_barcode_serum_replicate_drops)} barcode/serum-replicates for failing "
    f"{qc_thresholds['min_dilutions_per_barcode_serum_replicate']=}: "
    + str(min_dilutions_per_barcode_serum_replicate_drops)
)
qc_drops["barcode_serum_replicates"].update(
    {
        w: "min_dilutions_per_barcode_serum_replicate"
        for w in min_dilutions_per_barcode_serum_replicate_drops
    }
)
frac_infectivity = frac_infectivity[
    ~frac_infectivity.assign(
        barcode_serum_replicate=lambda x: x.apply(
            lambda r: (r["barcode"], r["serum_replicate"]), axis=1
        )
    )["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
]

Dropping 1 barcode/serum-replicates for failing qc_thresholds['min_dilutions_per_barcode_serum_replicate']=6: [('CCTTTCTCAAAACATA', 'FCI_12')]

Fit neutralization curves without applying QC to curves

First fit curves to all serum replicates, then we will apply QC on the curve fits. Note that the fitting is done to the fraction infectivities with the ceiling:

fits_noqc = neutcurve.CurveFits(
    frac_infectivity.rename(
        columns={
            "frac_infectivity_ceiling": "fraction infectivity",
            "concentration": "serum concentration",
        }
    ),
    conc_col="serum concentration",
    fracinf_col="fraction infectivity",
    virus_col="strain",
    serum_col="serum_replicate",
    replicate_col="barcode",
    fixtop=curvefit_params["fixtop"],
    fixbottom=curvefit_params["fixbottom"],
    fixslope=curvefit_params["fixslope"],
)

Determine which fits fail the curve fitting QC, and plot them. Note the plot indicates as failing QC any barcode / serum-replicate that fails, even if we are also specified to ignore the QC for that one (so it will not be removed later):

goodness_of_fit = curvefit_qc["goodness_of_fit"]

fit_params_noqc = (
    frac_infectivity.groupby(["serum_replicate", "barcode"], as_index=False)
    .aggregate(max_frac_infectivity=pd.NamedAgg("frac_infectivity_ceiling", "max"))
    .merge(
        fits_noqc.fitParams(average_only=False, no_average=True)[
            ["serum", "virus", "replicate", "r2", "rmsd"]
        ].rename(columns={"serum": "serum_replicate", "replicate": "barcode"}),
        validate="one_to_one",
    )
    .assign(
        fails_max_frac_infectivity_at_least=lambda x: (
            x["max_frac_infectivity"] < curvefit_qc["max_frac_infectivity_at_least"]
        ),
        fails_goodness_of_fit=lambda x: (
            (x["r2"] < goodness_of_fit["min_R2"])
            & (x["rmsd"] > goodness_of_fit["max_RMSD"])
        ),
        fails_qc=lambda x: (
            x["fails_max_frac_infectivity_at_least"] | x["fails_goodness_of_fit"]
        ),
        ignore_qc=lambda x: x.apply(
            lambda r: (
                (
                    r["serum_replicate"]
                    in curvefit_qc["serum_replicates_ignore_curvefit_qc"]
                )
                or (
                    (r["barcode"], r["serum_replicate"])
                    in curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"]
                )
            ),
            axis=1,
        ),
    )
)

print(f"Plotting barcode / serum-replicates that fail {curvefit_qc=}\n")

fit_params_noqc_base_chart = alt.Chart(fit_params_noqc).add_params(barcode_selection)
fit_params_noqc_chart = []
for prop, col in [
    ("max frac infectivity", "max_frac_infectivity"),
    ("curve fit R2", "r2"),
    ("curve fit RMSD", "rmsd"),
]:
    fit_params_noqc_chart.append(
        fit_params_noqc_base_chart.encode(
            alt.X(col, title=prop, scale=alt.Scale(nice=False, padding=4)),
            alt.Y("virus", title=None),
            alt.Fill("fails_qc"),
            alt.Column(
                "serum_replicate",
                title=None,
                header=alt.Header(
                    labelFontSize=12, labelFontStyle="bold", labelPadding=0
                ),
            ),
            strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
            size=alt.condition(barcode_selection, alt.value(55), alt.value(35)),
            tooltip=[
                alt.Tooltip(c, format=".3g") if fit_params_noqc[c].dtype == float else c
                for c in fit_params_noqc.columns
            ],
        )
        .mark_circle(stroke="black", strokeOpacity=1, fillOpacity=0.55)
        .properties(
            height=alt.Step(10),
            width=90,
            title=alt.TitleParams(f"{prop} for each barcode serum-replicate", dy=-2),
        )
    )

alt.vconcat(*fit_params_noqc_chart)

Plotting barcode / serum-replicates that fail curvefit_qc={'max_frac_infectivity_at_least': 0.0, 'goodness_of_fit': {'min_R2': 0.5, 'max_RMSD': 0.15}, 'serum_replicates_ignore_curvefit_qc': [], 'barcode_serum_replicates_ignore_curvefit_qc': []}

Now plot curves for all virus vs serum-replicates that have a barcode that fails any of the QC. In these plots, the suffix on the barcode name in the color key indicates if it passed or failed QC:

barcode_serum_replicates_fail_qc = fit_params_noqc.query("fails_qc").reset_index(
    drop=True
)
print(f"Here are barcode / serum-replicates that fail {curvefit_qc=}")
display(barcode_serum_replicates_fail_qc)

if len(barcode_serum_replicates_fail_qc):
    print(
        "\nCurves for virus vs serum-replicates with at least one failed barcode."
        "\nColor key labels indicate if barcodes failed or passed QC."
    )
    plots = {}
    ncol = 6
    for iplot, (serum, virus, failed_barcodes) in enumerate(
        barcode_serum_replicates_fail_qc.groupby(
            ["serum_replicate", "virus"], as_index=False
        )
        .aggregate(barcodes=pd.NamedAgg("barcode", list))
        .itertuples(index=False)
    ):
        passed_barcodes = [
            bc
            for bc in fits_noqc.replicates[serum, virus]
            if (bc not in failed_barcodes) and (bc != "average")
        ]
        curvelist = []
        assert len(CBMARKERS) >= len(failed_barcodes + passed_barcodes)
        assert len(CBPALETTE) >= len(failed_barcodes + passed_barcodes)
        for replicate, marker, color in zip(
            failed_barcodes + passed_barcodes, CBMARKERS, CBPALETTE
        ):
            curvelist.append(
                {
                    "serum": serum,
                    "virus": virus,
                    "replicate": replicate,
                    "label": replicate
                    + ("-fail" if replicate in failed_barcodes else "-pass"),
                    "color": color,
                    "marker": marker,
                }
            )
        plots[iplot // ncol, iplot % ncol] = (f"{serum} vs {virus}", curvelist)

    fig, _ = fits_noqc.plotGrid(
        plots,
        attempt_shared_legend=False,
        legendfontsize=8,
        titlesize=9,
        ticksize=10,
        draw_in_bounds=True,
    )
    display_curve_fig(fig, curve_display_method)
    plt.close(fig)

Here are barcode / serum-replicates that fail curvefit_qc={'max_frac_infectivity_at_least': 0.0, 'goodness_of_fit': {'min_R2': 0.5, 'max_RMSD': 0.15}, 'serum_replicates_ignore_curvefit_qc': [], 'barcode_serum_replicates_ignore_curvefit_qc': []}

	serum_replicate	barcode	max_frac_infectivity	virus	r2	rmsd	fails_max_frac_infectivity_at_least	fails_goodness_of_fit	fails_qc	ignore_qc
0	FCI_12	AAAGACCTTTAACTCT	1.000000	A/Singapore/INFIMH-16-0019/2016X-307A_H3N2	0.000000e+00	0.171395	False	True	True	False
1	FCI_12	AACCACCCCAGAGATG	1.000000	A/Kansas/14/2017_H3N2	0.000000e+00	0.287089	False	True	True	False
2	FCI_12	AACTTCCCTGACTGCT	1.000000	A/Victoria/46/2024_H3N2	3.447060e-01	0.205315	False	True	True	False
3	FCI_12	AAGTTAGTAGACCCAC	1.000000	A/Texas/ISC-1274/2025_H3N2	3.108775e-01	0.163159	False	True	True	False
4	FCI_12	ACGCAAATAGACCGAA	1.000000	A/Texas/50/2012X-223A_H3N2	3.130004e-01	0.180970	False	True	True	False
...	...	...	...	...	...	...	...	...	...	...
179	FCI_18	TCGATTACTAGCCGGA	1.000000	A/Switzerland/9715293/2013_H3N2	2.220446e-16	0.191672	False	True	True	False
180	FCI_18	TCTCAGCTCTTAGCCG	0.992548	A/Texas/ISC-1148/2025_H3N2	6.783546e-02	0.214681	False	True	True	False
181	FCI_18	TCTTAGAGTGAACGAT	0.977751	A/HongKong/4801/2014_H3N2	4.393800e-01	0.181943	False	True	True	False
182	FCI_18	TGTAATAGGCGTCACA	1.000000	A/Washington/UW-25728/2024_H3N2	2.700512e-01	0.162203	False	True	True	False
183	FCI_18	TTTCACAGAACCTATC	1.000000	A/Badajoz/18680568/2025_H3N2	1.110223e-16	0.153649	False	True	True	False

184 rows × 10 columns

Curves for virus vs serum-replicates with at least one failed barcode.
Color key labels indicate if barcodes failed or passed QC.

# drop barcode / serum-replicates failing QC
for qc_filter in ["max_frac_infectivity_at_least", "goodness_of_fit"]:
    fits_qc_drops = list(
        fit_params_noqc.query(f"fails_{qc_filter} and (not ignore_qc)")[
            ["barcode", "serum_replicate"]
        ].itertuples(index=False, name=None)
    )
    print(
        f"\nDropping {len(fits_qc_drops)} barcode/serum-replicates for failing "
        f"{qc_filter}={curvefit_qc[qc_filter]}: " + str(fits_qc_drops)
    )
    qc_drops["barcode_serum_replicates"].update({w: qc_filter for w in fits_qc_drops})
    frac_infectivity = frac_infectivity[
        ~frac_infectivity.assign(
            barcode_serum_replicate=lambda x: x.apply(
                lambda r: (r["barcode"], r["serum_replicate"]), axis=1
            )
        )["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
    ]
    fit_params_noqc = fit_params_noqc[
        ~fit_params_noqc.assign(
            barcode_serum_replicate=lambda x: x.apply(
                lambda r: (r["barcode"], r["serum_replicate"]), axis=1
            )
        )["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
    ]

Dropping 0 barcode/serum-replicates for failing max_frac_infectivity_at_least=0.0: []

Dropping 184 barcode/serum-replicates for failing goodness_of_fit={'min_R2': 0.5, 'max_RMSD': 0.15}: [('AAAGACCTTTAACTCT', 'FCI_12'), ('AACCACCCCAGAGATG', 'FCI_12'), ('AACTTCCCTGACTGCT', 'FCI_12'), ('AAGTTAGTAGACCCAC', 'FCI_12'), ('ACGCAAATAGACCGAA', 'FCI_12'), ('AGACCATCGCACCCAA', 'FCI_12'), ('AGACCGCCAGTTTCGT', 'FCI_12'), ('AGTTCCATAGGCATGG', 'FCI_12'), ('ATAACGTTTGTGCAAA', 'FCI_12'), ('ATAGAAAATTATCCGC', 'FCI_12'), ('ATTAGATTATAACGTA', 'FCI_12'), ('CACAGACAATAAAAAA', 'FCI_12'), ('CATAAAAGACTGTATA', 'FCI_12'), ('CATGGGAATTGCCACT', 'FCI_12'), ('CGAAAACATTACAAAT', 'FCI_12'), ('CGGGAATCTCCCATAC', 'FCI_12'), ('CGGTCGGGACTCATCT', 'FCI_12'), ('CGTACAGTGTAATCGA', 'FCI_12'), ('CTCCTAGGGGACGATT', 'FCI_12'), ('GTAATTCGCATGCGGA', 'FCI_12'), ('TAAAAAGCCTCCATGA', 'FCI_12'), ('TAGCATTGTCGGAAAG', 'FCI_12'), ('TATTCCTAACTAGCGA', 'FCI_12'), ('TCAATCGGGGGCTAAA', 'FCI_12'), ('TGATCTTTTACATTTA', 'FCI_12'), ('TTCATCAAGTTGGTGC', 'FCI_12'), ('TTTATATCCAACACCA', 'FCI_12'), ('TTTCACAGAACCTATC', 'FCI_12'), ('AAATTCACAATATCCA', 'FCI_13'), ('AACCACCCCAGAGATG', 'FCI_13'), ('AACCGTACCGCGTTTA', 'FCI_13'), ('AAGAAGCTATAGAAGT', 'FCI_13'), ('AAGCCCAGCGGGTGAT', 'FCI_13'), ('AATGAAACAATCGAAC', 'FCI_13'), ('ACGCAAATAGACCGAA', 'FCI_13'), ('ACTCTGGCTCGCTAAT', 'FCI_13'), ('ATAGAAAATTATCCGC', 'FCI_13'), ('ATTTACTCATTATACG', 'FCI_13'), ('CACAGACAATAAAAAA', 'FCI_13'), ('CAGATAGTGATGAACA', 'FCI_13'), ('CCCCTCCTCTAAAGTT', 'FCI_13'), ('CCTTGATGCATTCCCG', 'FCI_13'), ('CGAAAACATTACAAAT', 'FCI_13'), ('CGTTCAGCGATAACGG', 'FCI_13'), ('CTAGCACAGCGTAGGC', 'FCI_13'), ('GACGGGATGGGCACGT', 'FCI_13'), ('GCAACGAGGTGTAACC', 'FCI_13'), ('GCATCCTCAACTCCTA', 'FCI_13'), ('GTAATTCGCATGCGGA', 'FCI_13'), ('GTTGCTCCGACACGCC', 'FCI_13'), ('TTTCACAGAACCTATC', 'FCI_13'), ('AACCACCCCAGAGATG', 'FCI_14'), ('AAGAAGCTATAGAAGT', 'FCI_14'), ('AAGATTGATTGAAGTT', 'FCI_14'), ('AGACCGCCAGTTTCGT', 'FCI_14'), ('AGCGACATCGCCCTTT', 'FCI_14'), ('ATAACGTTTGTGCAAA', 'FCI_14'), ('ATTAGATTATAACGTA', 'FCI_14'), ('CATGGGAATTGCCACT', 'FCI_14'), ('CCTTTCTCAAAACATA', 'FCI_14'), ('CGGGGACAAGATTGTA', 'FCI_14'), ('CGGTCGGGACTCATCT', 'FCI_14'), ('CGTACGTATGTCCCAG', 'FCI_14'), ('CTGAGCTGCCAATAAG', 'FCI_14'), ('GAAGTGCGTATTGAGT', 'FCI_14'), ('GCAACGAGGTGTAACC', 'FCI_14'), ('GCATCCTCAACTCCTA', 'FCI_14'), ('GTAATTCGCATGCGGA', 'FCI_14'), ('TCGATTACTAGCCGGA', 'FCI_14'), ('TCTCAGCTCTTAGCCG', 'FCI_14'), ('TCTGGAAACGATCCCC', 'FCI_14'), ('TGGTCCGCTTCATGCT', 'FCI_14'), ('AACTGCGTTCATCGAT', 'FCI_15'), ('ACAAGATTCGGGGGAC', 'FCI_15'), ('ACTCTGGCTCGCTAAT', 'FCI_15'), ('AGACCATCGCACCCAA', 'FCI_15'), ('AGCGACATCGCCCTTT', 'FCI_15'), ('AGTGTTGAATAGGCGA', 'FCI_15'), ('CACCTAGGATCGCACT', 'FCI_15'), ('CAGGCTCTAGAGCTCT', 'FCI_15'), ('CATAAAAGACTGTATA', 'FCI_15'), ('CATGGGAATTGCCACT', 'FCI_15'), ('CCCCTCCTCTAAAGTT', 'FCI_15'), ('CCGCATTAGCGGGAGG', 'FCI_15'), ('CCGCGCACGTTTAGAG', 'FCI_15'), ('CGAAAACATTACAAAT', 'FCI_15'), ('CGGGAATCTCCCATAC', 'FCI_15'), ('CGTACGTATGTCCCAG', 'FCI_15'), ('CTCAATGTCGTAGGAT', 'FCI_15'), ('CTCCTAGGGGACGATT', 'FCI_15'), ('CTTAGGTATTATATGC', 'FCI_15'), ('GTAATTCGCATGCGGA', 'FCI_15'), ('GTCCGTCAGCATAAAC', 'FCI_15'), ('TACATACCGACGCAGT', 'FCI_15'), ('TCTCAGCTCTTAGCCG', 'FCI_15'), ('TCTTAGAGTGAACGAT', 'FCI_15'), ('TCTTATTAGGCGGCAT', 'FCI_15'), ('TTGACTCACCGAATAA', 'FCI_15'), ('TTGCAATTGAAACATA', 'FCI_15'), ('AAAGTAGCAGAGGATT', 'FCI_16'), ('AAATTCACAATATCCA', 'FCI_16'), ('AACCGTACCGCGTTTA', 'FCI_16'), ('AAGCCCAGCGGGTGAT', 'FCI_16'), ('AAGTATTGCTACACAT', 'FCI_16'), ('AGCGACATCGCCCTTT', 'FCI_16'), ('CCGCATTAGCGGGAGG', 'FCI_16'), ('CGGGAATCTCCCATAC', 'FCI_16'), ('CGGGGACAAGATTGTA', 'FCI_16'), ('CGTTAACGGCCTATCC', 'FCI_16'), ('CGTTTTTGGTTCGAGG', 'FCI_16'), ('GTAATTCGCATGCGGA', 'FCI_16'), ('GTCGCCGCTAATCCGA', 'FCI_16'), ('TCAATCGGGGGCTAAA', 'FCI_16'), ('TTCATCAAGTTGGTGC', 'FCI_16'), ('TTTCACAGAACCTATC', 'FCI_16'), ('AACCACCCCAGAGATG', 'FCI_17'), ('AACCGTACCGCGTTTA', 'FCI_17'), ('AACTTCCGTCGCCTGA', 'FCI_17'), ('AAGAAGACTTTGTGAT', 'FCI_17'), ('AAGCCCAGCGGGTGAT', 'FCI_17'), ('AAGTATTGCTACACAT', 'FCI_17'), ('ACGTGTCTCCGAGCAA', 'FCI_17'), ('ATGGCCCACGGGCATA', 'FCI_17'), ('ATTTACTCATTATACG', 'FCI_17'), ('CACCAATCTTCGAACT', 'FCI_17'), ('CAGAACCTCGTTGTCT', 'FCI_17'), ('CAGATAGTGATGAACA', 'FCI_17'), ('CAGGCTCTAGAGCTCT', 'FCI_17'), ('CATAAAAGACTGTATA', 'FCI_17'), ('CCGCATTAGCGGGAGG', 'FCI_17'), ('CGCACTTTACGAGACA', 'FCI_17'), ('CGGACCCTAGATGGTA', 'FCI_17'), ('CGGGAATCTCCCATAC', 'FCI_17'), ('CGGGGACAAGATTGTA', 'FCI_17'), ('CGTACGTATGTCCCAG', 'FCI_17'), ('CGTTAACGGCCTATCC', 'FCI_17'), ('CTGAGGGATTCAACTC', 'FCI_17'), ('GAAAGCCCCGTGCAAT', 'FCI_17'), ('GCAGCGTGCCGGTCAT', 'FCI_17'), ('GCCGCTGCGGCGTGTG', 'FCI_17'), ('TACATACCGACGCAGT', 'FCI_17'), ('TACCAATGTCATTTGA', 'FCI_17'), ('TATCCAAGGGACGGAC', 'FCI_17'), ('TATTCCTAACTAGCGA', 'FCI_17'), ('TCAATCGGGGGCTAAA', 'FCI_17'), ('TCTCAGCTCTTAGCCG', 'FCI_17'), ('TCTGGAAACGATCCCC', 'FCI_17'), ('TGAGTTCATAGCTCCA', 'FCI_17'), ('TGGTCCGCTTCATGCT', 'FCI_17'), ('TTGAAAAAATCATAAA', 'FCI_17'), ('TTGCAATTGAAACATA', 'FCI_17'), ('TTTCAGCGTTGTTTTG', 'FCI_17'), ('AACCACCCCAGAGATG', 'FCI_18'), ('AACCGTACCGCGTTTA', 'FCI_18'), ('AACTTCCCTGACTGCT', 'FCI_18'), ('AAGATTGATTGAAGTT', 'FCI_18'), ('AAGCCCAGCGGGTGAT', 'FCI_18'), ('AAGTATTGCTACACAT', 'FCI_18'), ('ACAGTACGATCTACGC', 'FCI_18'), ('AGACCATCGCACCCAA', 'FCI_18'), ('AGCGACATCGCCCTTT', 'FCI_18'), ('AGTCCTATCCTCAAAT', 'FCI_18'), ('ATAGAAAATTATCCGC', 'FCI_18'), ('ATGGCCCACGGGCATA', 'FCI_18'), ('ATTAGATTATAACGTA', 'FCI_18'), ('CAATTCGCCGTTCCCC', 'FCI_18'), ('CACCTAGGATCGCACT', 'FCI_18'), ('CCGCATTAGCGGGAGG', 'FCI_18'), ('CCGCGCACGTTTAGAG', 'FCI_18'), ('CGATCTTTACGAAAAA', 'FCI_18'), ('CGGGAATCTCCCATAC', 'FCI_18'), ('CGTACGTATGTCCCAG', 'FCI_18'), ('CGTTTTTGGTTCGAGG', 'FCI_18'), ('CTTAGGTATTATATGC', 'FCI_18'), ('GAAAGCCCCGTGCAAT', 'FCI_18'), ('GGTTAACTTTGGAAGC', 'FCI_18'), ('GTTGCTCCGACACGCC', 'FCI_18'), ('TCACGACTCGACTAAC', 'FCI_18'), ('TCCCCGTGGTTTGACA', 'FCI_18'), ('TCGATTACTAGCCGGA', 'FCI_18'), ('TCTCAGCTCTTAGCCG', 'FCI_18'), ('TCTTAGAGTGAACGAT', 'FCI_18'), ('TGTAATAGGCGTCACA', 'FCI_18'), ('TTTCACAGAACCTATC', 'FCI_18')]

Fit neutralization curves after applying QC

No we re-fit curves after applying all the QC:

fits_qc = neutcurve.CurveFits(
    frac_infectivity.rename(
        columns={
            "frac_infectivity_ceiling": "fraction infectivity",
            "concentration": "serum concentration",
        }
    ),
    conc_col="serum concentration",
    fracinf_col="fraction infectivity",
    virus_col="strain",
    serum_col="serum",
    replicate_col="plate_barcode",
    fixtop=curvefit_params["fixtop"],
    fixbottom=curvefit_params["fixbottom"],
    fixslope=curvefit_params["fixslope"],
)

fit_params_qc = fits_qc.fitParams(average_only=False, no_average=True)
assert len(fit_params_qc) <= len(
    fits_noqc.fitParams(average_only=False, no_average=True)
)

print(f"Assigning fits for this plate to {group}")
fit_params_qc.insert(0, "group", group)

Assigning fits for this plate to FCI

Plot all the curves that passed QC:

if fits_qc.sera:
    fig, _ = fits_qc.plotReplicates(
        attempt_shared_legend=False,
        legendfontsize=8,
        titlesize=9,
        ticksize=10,
        ncol=6,
        draw_in_bounds=True,
    )
    display_curve_fig(fig, curve_display_method)
    plt.close(fig)
else:
    print("No sera passed QC.")

Save results to files

print(f"Writing fraction infectivities to {frac_infectivity_csv}")
(
    frac_infectivity[
        [
            "serum",
            "strain",
            "plate_barcode",
            "dilution_factor",
            "frac_infectivity_raw",
            "frac_infectivity_ceiling",
        ]
    ]
    .sort_values(["serum", "plate_barcode", "dilution_factor"])
    .to_csv(frac_infectivity_csv, index=False, float_format="%.4g")
)

print(f"\nWriting fit parameters to {fits_csv}")
(
    fit_params_qc.drop(columns=["nreplicates", "ic50_str"]).to_csv(
        fits_csv, index=False, float_format="%.4g"
    )
)

print(f"\nPickling neutcurve.CurveFits object for these data to {fits_pickle}")
with open(fits_pickle, "wb") as f:
    pickle.dump(fits_qc, f)

print(f"\nWriting QC drops to {qc_drops_yaml}")


def tup_to_str(x):
    return " ".join(x) if isinstance(x, tuple) else x


qc_drops_for_yaml = {
    key: {tup_to_str(key2): val2 for key2, val2 in val.items()}
    for key, val in qc_drops.items()
}
with open(qc_drops_yaml, "w") as f:
    yaml.YAML(typ="rt").dump(qc_drops_for_yaml, f)
print("\nHere are the QC drops:\n***************************")
yaml.YAML(typ="rt").dump(qc_drops_for_yaml, sys.stdout)

Writing fraction infectivities to results/plates/plate7_FCI/frac_infectivity.csv

Writing fit parameters to results/plates/plate7_FCI/curvefits.csv

Pickling neutcurve.CurveFits object for these data to results/plates/plate7_FCI/curvefits.pickle

Writing QC drops to results/plates/plate7_FCI/qc_drops.yml

Here are the QC drops:
***************************
wells:
  F2: avg_barcode_counts_per_well
barcodes: {}
barcode_wells:
  CCTTTCTCAAAACATA A1: max_frac_infectivity_per_viral_barcode_well
  CCTTTCTCAAAACATA E1: max_frac_infectivity_per_viral_barcode_well
  CCTTTCTCAAAACATA H1: max_frac_infectivity_per_viral_barcode_well
  CCTTTCTCAAAACATA B2: max_frac_infectivity_per_viral_barcode_well
  TGTTGTAATCTGAATA A3: max_frac_infectivity_per_viral_barcode_well
  CCTTTCTCAAAACATA A3: max_frac_infectivity_per_viral_barcode_well
  ATTTACTCATTATACG B3: max_frac_infectivity_per_viral_barcode_well
  GATTCAGATGCCCACC D3: max_frac_infectivity_per_viral_barcode_well
  TATCCAAGGGACGGAC E3: max_frac_infectivity_per_viral_barcode_well
  CGATCTTTACGAAAAA E3: max_frac_infectivity_per_viral_barcode_well
  GCCGCTGCGGCGTGTG F3: max_frac_infectivity_per_viral_barcode_well
  AGTCCTATCCTCAAAT G3: max_frac_infectivity_per_viral_barcode_well
  CCTTTCTCAAAACATA A4: max_frac_infectivity_per_viral_barcode_well
  CCTTTCTCAAAACATA D4: max_frac_infectivity_per_viral_barcode_well
  TCACGACTCGACTAAC F4: max_frac_infectivity_per_viral_barcode_well
  TGTTGTAATCTGAATA B5: max_frac_infectivity_per_viral_barcode_well
  GATCGCCACTGATAAG C5: max_frac_infectivity_per_viral_barcode_well
  CCTTTCTCAAAACATA C5: max_frac_infectivity_per_viral_barcode_well
  GTAATTCGCATGCGGA D5: max_frac_infectivity_per_viral_barcode_well
  GATTCAGATGCCCACC E5: max_frac_infectivity_per_viral_barcode_well
  CCTTTCTCAAAACATA F5: max_frac_infectivity_per_viral_barcode_well
  CCGCATTAGCGGGAGG D6: max_frac_infectivity_per_viral_barcode_well
  CACAGACAATAAAAAA G6: max_frac_infectivity_per_viral_barcode_well
  CTCTTACGCTCCTACG G6: max_frac_infectivity_per_viral_barcode_well
  TTGACTCACCGAATAA H6: max_frac_infectivity_per_viral_barcode_well
  AAAGACCTTTAACTCT H7: max_frac_infectivity_per_viral_barcode_well
barcode_serum_replicates:
  CCTTTCTCAAAACATA FCI_12: min_dilutions_per_barcode_serum_replicate
  AAAGACCTTTAACTCT FCI_12: goodness_of_fit
  AACCACCCCAGAGATG FCI_12: goodness_of_fit
  AACTTCCCTGACTGCT FCI_12: goodness_of_fit
  AAGTTAGTAGACCCAC FCI_12: goodness_of_fit
  ACGCAAATAGACCGAA FCI_12: goodness_of_fit
  AGACCATCGCACCCAA FCI_12: goodness_of_fit
  AGACCGCCAGTTTCGT FCI_12: goodness_of_fit
  AGTTCCATAGGCATGG FCI_12: goodness_of_fit
  ATAACGTTTGTGCAAA FCI_12: goodness_of_fit
  ATAGAAAATTATCCGC FCI_12: goodness_of_fit
  ATTAGATTATAACGTA FCI_12: goodness_of_fit
  CACAGACAATAAAAAA FCI_12: goodness_of_fit
  CATAAAAGACTGTATA FCI_12: goodness_of_fit
  CATGGGAATTGCCACT FCI_12: goodness_of_fit
  CGAAAACATTACAAAT FCI_12: goodness_of_fit
  CGGGAATCTCCCATAC FCI_12: goodness_of_fit
  CGGTCGGGACTCATCT FCI_12: goodness_of_fit
  CGTACAGTGTAATCGA FCI_12: goodness_of_fit
  CTCCTAGGGGACGATT FCI_12: goodness_of_fit
  GTAATTCGCATGCGGA FCI_12: goodness_of_fit
  TAAAAAGCCTCCATGA FCI_12: goodness_of_fit
  TAGCATTGTCGGAAAG FCI_12: goodness_of_fit
  TATTCCTAACTAGCGA FCI_12: goodness_of_fit
  TCAATCGGGGGCTAAA FCI_12: goodness_of_fit
  TGATCTTTTACATTTA FCI_12: goodness_of_fit
  TTCATCAAGTTGGTGC FCI_12: goodness_of_fit
  TTTATATCCAACACCA FCI_12: goodness_of_fit
  TTTCACAGAACCTATC FCI_12: goodness_of_fit
  AAATTCACAATATCCA FCI_13: goodness_of_fit
  AACCACCCCAGAGATG FCI_13: goodness_of_fit
  AACCGTACCGCGTTTA FCI_13: goodness_of_fit
  AAGAAGCTATAGAAGT FCI_13: goodness_of_fit
  AAGCCCAGCGGGTGAT FCI_13: goodness_of_fit
  AATGAAACAATCGAAC FCI_13: goodness_of_fit
  ACGCAAATAGACCGAA FCI_13: goodness_of_fit
  ACTCTGGCTCGCTAAT FCI_13: goodness_of_fit
  ATAGAAAATTATCCGC FCI_13: goodness_of_fit
  ATTTACTCATTATACG FCI_13: goodness_of_fit
  CACAGACAATAAAAAA FCI_13: goodness_of_fit
  CAGATAGTGATGAACA FCI_13: goodness_of_fit
  CCCCTCCTCTAAAGTT FCI_13: goodness_of_fit
  CCTTGATGCATTCCCG FCI_13: goodness_of_fit
  CGAAAACATTACAAAT FCI_13: goodness_of_fit
  CGTTCAGCGATAACGG FCI_13: goodness_of_fit
  CTAGCACAGCGTAGGC FCI_13: goodness_of_fit
  GACGGGATGGGCACGT FCI_13: goodness_of_fit
  GCAACGAGGTGTAACC FCI_13: goodness_of_fit
  GCATCCTCAACTCCTA FCI_13: goodness_of_fit
  GTAATTCGCATGCGGA FCI_13: goodness_of_fit
  GTTGCTCCGACACGCC FCI_13: goodness_of_fit
  TTTCACAGAACCTATC FCI_13: goodness_of_fit
  AACCACCCCAGAGATG FCI_14: goodness_of_fit
  AAGAAGCTATAGAAGT FCI_14: goodness_of_fit
  AAGATTGATTGAAGTT FCI_14: goodness_of_fit
  AGACCGCCAGTTTCGT FCI_14: goodness_of_fit
  AGCGACATCGCCCTTT FCI_14: goodness_of_fit
  ATAACGTTTGTGCAAA FCI_14: goodness_of_fit
  ATTAGATTATAACGTA FCI_14: goodness_of_fit
  CATGGGAATTGCCACT FCI_14: goodness_of_fit
  CCTTTCTCAAAACATA FCI_14: goodness_of_fit
  CGGGGACAAGATTGTA FCI_14: goodness_of_fit
  CGGTCGGGACTCATCT FCI_14: goodness_of_fit
  CGTACGTATGTCCCAG FCI_14: goodness_of_fit
  CTGAGCTGCCAATAAG FCI_14: goodness_of_fit
  GAAGTGCGTATTGAGT FCI_14: goodness_of_fit
  GCAACGAGGTGTAACC FCI_14: goodness_of_fit
  GCATCCTCAACTCCTA FCI_14: goodness_of_fit
  GTAATTCGCATGCGGA FCI_14: goodness_of_fit
  TCGATTACTAGCCGGA FCI_14: goodness_of_fit
  TCTCAGCTCTTAGCCG FCI_14: goodness_of_fit
  TCTGGAAACGATCCCC FCI_14: goodness_of_fit
  TGGTCCGCTTCATGCT FCI_14: goodness_of_fit
  AACTGCGTTCATCGAT FCI_15: goodness_of_fit
  ACAAGATTCGGGGGAC FCI_15: goodness_of_fit
  ACTCTGGCTCGCTAAT FCI_15: goodness_of_fit
  AGACCATCGCACCCAA FCI_15: goodness_of_fit
  AGCGACATCGCCCTTT FCI_15: goodness_of_fit
  AGTGTTGAATAGGCGA FCI_15: goodness_of_fit
  CACCTAGGATCGCACT FCI_15: goodness_of_fit
  CAGGCTCTAGAGCTCT FCI_15: goodness_of_fit
  CATAAAAGACTGTATA FCI_15: goodness_of_fit
  CATGGGAATTGCCACT FCI_15: goodness_of_fit
  CCCCTCCTCTAAAGTT FCI_15: goodness_of_fit
  CCGCATTAGCGGGAGG FCI_15: goodness_of_fit
  CCGCGCACGTTTAGAG FCI_15: goodness_of_fit
  CGAAAACATTACAAAT FCI_15: goodness_of_fit
  CGGGAATCTCCCATAC FCI_15: goodness_of_fit
  CGTACGTATGTCCCAG FCI_15: goodness_of_fit
  CTCAATGTCGTAGGAT FCI_15: goodness_of_fit
  CTCCTAGGGGACGATT FCI_15: goodness_of_fit
  CTTAGGTATTATATGC FCI_15: goodness_of_fit
  GTAATTCGCATGCGGA FCI_15: goodness_of_fit
  GTCCGTCAGCATAAAC FCI_15: goodness_of_fit
  TACATACCGACGCAGT FCI_15: goodness_of_fit
  TCTCAGCTCTTAGCCG FCI_15: goodness_of_fit
  TCTTAGAGTGAACGAT FCI_15: goodness_of_fit
  TCTTATTAGGCGGCAT FCI_15: goodness_of_fit
  TTGACTCACCGAATAA FCI_15: goodness_of_fit
  TTGCAATTGAAACATA FCI_15: goodness_of_fit
  AAAGTAGCAGAGGATT FCI_16: goodness_of_fit
  AAATTCACAATATCCA FCI_16: goodness_of_fit
  AACCGTACCGCGTTTA FCI_16: goodness_of_fit
  AAGCCCAGCGGGTGAT FCI_16: goodness_of_fit
  AAGTATTGCTACACAT FCI_16: goodness_of_fit
  AGCGACATCGCCCTTT FCI_16: goodness_of_fit
  CCGCATTAGCGGGAGG FCI_16: goodness_of_fit
  CGGGAATCTCCCATAC FCI_16: goodness_of_fit
  CGGGGACAAGATTGTA FCI_16: goodness_of_fit
  CGTTAACGGCCTATCC FCI_16: goodness_of_fit
  CGTTTTTGGTTCGAGG FCI_16: goodness_of_fit
  GTAATTCGCATGCGGA FCI_16: goodness_of_fit
  GTCGCCGCTAATCCGA FCI_16: goodness_of_fit
  TCAATCGGGGGCTAAA FCI_16: goodness_of_fit
  TTCATCAAGTTGGTGC FCI_16: goodness_of_fit
  TTTCACAGAACCTATC FCI_16: goodness_of_fit
  AACCACCCCAGAGATG FCI_17: goodness_of_fit
  AACCGTACCGCGTTTA FCI_17: goodness_of_fit
  AACTTCCGTCGCCTGA FCI_17: goodness_of_fit
  AAGAAGACTTTGTGAT FCI_17: goodness_of_fit
  AAGCCCAGCGGGTGAT FCI_17: goodness_of_fit
  AAGTATTGCTACACAT FCI_17: goodness_of_fit
  ACGTGTCTCCGAGCAA FCI_17: goodness_of_fit
  ATGGCCCACGGGCATA FCI_17: goodness_of_fit
  ATTTACTCATTATACG FCI_17: goodness_of_fit
  CACCAATCTTCGAACT FCI_17: goodness_of_fit
  CAGAACCTCGTTGTCT FCI_17: goodness_of_fit
  CAGATAGTGATGAACA FCI_17: goodness_of_fit
  CAGGCTCTAGAGCTCT FCI_17: goodness_of_fit
  CATAAAAGACTGTATA FCI_17: goodness_of_fit
  CCGCATTAGCGGGAGG FCI_17: goodness_of_fit
  CGCACTTTACGAGACA FCI_17: goodness_of_fit
  CGGACCCTAGATGGTA FCI_17: goodness_of_fit
  CGGGAATCTCCCATAC FCI_17: goodness_of_fit
  CGGGGACAAGATTGTA FCI_17: goodness_of_fit
  CGTACGTATGTCCCAG FCI_17: goodness_of_fit
  CGTTAACGGCCTATCC FCI_17: goodness_of_fit
  CTGAGGGATTCAACTC FCI_17: goodness_of_fit
  GAAAGCCCCGTGCAAT FCI_17: goodness_of_fit
  GCAGCGTGCCGGTCAT FCI_17: goodness_of_fit
  GCCGCTGCGGCGTGTG FCI_17: goodness_of_fit
  TACATACCGACGCAGT FCI_17: goodness_of_fit
  TACCAATGTCATTTGA FCI_17: goodness_of_fit
  TATCCAAGGGACGGAC FCI_17: goodness_of_fit
  TATTCCTAACTAGCGA FCI_17: goodness_of_fit
  TCAATCGGGGGCTAAA FCI_17: goodness_of_fit
  TCTCAGCTCTTAGCCG FCI_17: goodness_of_fit
  TCTGGAAACGATCCCC FCI_17: goodness_of_fit
  TGAGTTCATAGCTCCA FCI_17: goodness_of_fit
  TGGTCCGCTTCATGCT FCI_17: goodness_of_fit
  TTGAAAAAATCATAAA FCI_17: goodness_of_fit
  TTGCAATTGAAACATA FCI_17: goodness_of_fit
  TTTCAGCGTTGTTTTG FCI_17: goodness_of_fit
  AACCACCCCAGAGATG FCI_18: goodness_of_fit
  AACCGTACCGCGTTTA FCI_18: goodness_of_fit
  AACTTCCCTGACTGCT FCI_18: goodness_of_fit
  AAGATTGATTGAAGTT FCI_18: goodness_of_fit
  AAGCCCAGCGGGTGAT FCI_18: goodness_of_fit
  AAGTATTGCTACACAT FCI_18: goodness_of_fit
  ACAGTACGATCTACGC FCI_18: goodness_of_fit
  AGACCATCGCACCCAA FCI_18: goodness_of_fit
  AGCGACATCGCCCTTT FCI_18: goodness_of_fit
  AGTCCTATCCTCAAAT FCI_18: goodness_of_fit
  ATAGAAAATTATCCGC FCI_18: goodness_of_fit
  ATGGCCCACGGGCATA FCI_18: goodness_of_fit
  ATTAGATTATAACGTA FCI_18: goodness_of_fit
  CAATTCGCCGTTCCCC FCI_18: goodness_of_fit
  CACCTAGGATCGCACT FCI_18: goodness_of_fit
  CCGCATTAGCGGGAGG FCI_18: goodness_of_fit
  CCGCGCACGTTTAGAG FCI_18: goodness_of_fit
  CGATCTTTACGAAAAA FCI_18: goodness_of_fit
  CGGGAATCTCCCATAC FCI_18: goodness_of_fit
  CGTACGTATGTCCCAG FCI_18: goodness_of_fit
  CGTTTTTGGTTCGAGG FCI_18: goodness_of_fit
  CTTAGGTATTATATGC FCI_18: goodness_of_fit
  GAAAGCCCCGTGCAAT FCI_18: goodness_of_fit
  GGTTAACTTTGGAAGC FCI_18: goodness_of_fit
  GTTGCTCCGACACGCC FCI_18: goodness_of_fit
  TCACGACTCGACTAAC FCI_18: goodness_of_fit
  TCCCCGTGGTTTGACA FCI_18: goodness_of_fit
  TCGATTACTAGCCGGA FCI_18: goodness_of_fit
  TCTCAGCTCTTAGCCG FCI_18: goodness_of_fit
  TCTTAGAGTGAACGAT FCI_18: goodness_of_fit
  TGTAATAGGCGTCACA FCI_18: goodness_of_fit
  TTTCACAGAACCTATC FCI_18: goodness_of_fit
serum_replicates: {}